Joint-embedding predictive architectures (JEPAs) have recently emerged as general-purpose visual world models for control. A prominent example is LeWorldModel (LeWM), which trains end-to-end from pixels using an isotropic Gaussian anti-collapse regularizer (SIGReg) and plans by minimizing the squared Euclidean distance between predicted and goal embeddings. In this work, we investigate the test-time robustness of this latent planning interface. Analyzing the learned metric on Push-T, we first find that task-relevant object state heavily dominates latent distance: shifting the block by a single pixel displaces the embedding $3\times$ further than an equivalent agent shift, and swapping the block pose alone accounts for $99\%$ of the distance between scene pairs. However, because this pose information is distributed across a curved, high-dimensional manifold rather than a fixed linear subspace, the encoder cannot be trivially edited post-hoc to isolate or redirect it. We next demonstrate that this geometry is highly vulnerable at test time. A white-box $\ell_\infty$ perturbation of just $ε{=}4/255$ applied to a single input frame causes the encoder to register the block at an arbitrary, attacker-chosen pose in $73$--$95\%$ of scenes without disturbing the encoded agent state. Perturbing either the goal image or the live camera observation drops Push-T planning success from $87\%$ to $32\%$, whereas uniform random noise at twice the magnitude has negligible impact. This vulnerability persists across all four standard LeWM benchmarks (Push-T, Two-Room, Reacher, and OGBench-Cube). At $ε{=}8/255$, the attack bridges $82$--$97\%$ of the squared latent distance to the target embedding in every environment, inducing planning degradation proportional to each task's reliance on the spoofed entity.

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